On the use of reactive multiparticle collision dynamics to gather particulate level information from simulations of epidemic models

This paper discusses the application of reactive multiparticle collision (RMPC) dynamics, a particle-based method, to epidemic models. First, we consider a susceptible-infectious-recovered framework to obtain data on contacts of susceptibles with infectious people in a population. It is found that the number of contacts increases and the contact duration decreases with increases in the disease transmission rate and average population speed. Next, we obtain reinfection statistics for a general infectious disease from RMPC simulations of a susceptible-infectious-recovered-susceptible model. Finally, we simulate a susceptible-exposed-infectious-recovered model and gather the exposure, infection, and recovery time for the individuals in the population under consideration. It is worth mentioning that we can collect data in the form of average contact duration, average initial infection time, etc., from RMPC simulations of these models, which is not possible with population-based stochastic models, or deterministic systems. This study provides quantitative insights on the potential of RMPC to simulate epidemic models and motivates future efforts for its application in the field of mathematical epidemiology.

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Publication Details

Journal
AIP Advances
Published
2024-09-01
DOI
https://doi.org/10.1063/5.0223361
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79

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article

On the use of reactive multiparticle collision dynamics to gather particulate level information from simulations of epidemic models

Katrin Rohlf, Zaib Un Nisa Memon
2 citations
AIP Advances
COVID-19 epidemiological studies
0.79
article

On the use of reactive multiparticle collision dynamics to gather particulate level information from simulations of epidemic models

Katrin Rohlf, Zaib Un Nisa Memon
article en
2 citations

Abstract

This paper discusses the application of reactive multiparticle collision (RMPC) dynamics, a particle-based method, to epidemic models. First, we consider a susceptible-infectious-recovered framework to obtain data on contacts of susceptibles with infectious people in a population. It is found that the number of contacts increases and the contact duration decreases with increases in the disease transmission rate and average population speed. Next, we obtain reinfection statistics for a general infectious disease from RMPC simulations of a susceptible-infectious-recovered-susceptible model. Finally, we simulate a susceptible-exposed-infectious-recovered model and gather the exposure, infection, and recovery time for the individuals in the population under consideration. It is worth mentioning that we can collect data in the form of average contact duration, average initial infection time, etc., from RMPC simulations of these models, which is not possible with population-based stochastic models, or deterministic systems. This study provides quantitative insights on the potential of RMPC to simulate epidemic models and motivates future efforts for its application in the field of mathematical epidemiology.

AIP AdvancesVol. 14(9)
Toronto Metropolitan University (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 31%
COVID-19 epidemiological studies
0.79
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